GPU Algorithms for Efficient Exascale Discretizations
Center for Applied Scientific Computing, Lawrence Livermore National Laboratory, Livermore, CA 94550
arXiv:2109.05072 [cs.DC], (10 Sep 2021)
@misc{abdelfattah2021gpu,
title={GPU Algorithms for Efficient Exascale Discretizations},
author={Ahmad Abdelfattah and Valeria Barra and Natalie Beams and Ryan Bleile and Jed Brown and Jean-Sylvain Camier and Robert Carson and Noel Chalmers and Veselin Dobrev and Yohann Dudouit and Paul Fischer and Ali Karakus and Stefan Kerkemeier and Tzanio Kolev and Yu-Hsiang Lan and Elia Merzari and Misun Min and Malachi Phillips and Thilina Rathnayake and Robert Rieben and Thomas Stitt and Ananias Tomboulides and Stanimire Tomov and Vladimir Tomov and Arturo Vargas and Tim Warburton and Kenneth Weiss},
year={2021},
eprint={2109.05072},
archivePrefix={arXiv},
primaryClass={cs.DC}
}
In this paper we describe the research and development activities in the Center for Efficient Exascale Discretization within the US Exascale Computing Project, targeting state-of-the-art high-order finite-element algorithms for high-order applications on GPU-accelerated platforms. We discuss the GPU developments in several components of the CEED software stack, including the libCEED, MAGMA, MFEM, libParanumal, and Nek projects. We report performance and capability improvements in several CEED-enabled applications on both NVIDIA and AMD GPU systems.
September 19, 2021 by hgpu
